Scholarlink

<p>Chronic Insomnia is a prevalent sleep disorder that remains difficult to diagnose due to subjective symptoms and heterogeneous presentations. The most severe form, insomnia with short sleep duration (ISSD), is defined by a total sleep time of less than six hours on polysomnography. However, objective assessments are rarely recommended in diagnostic guidelines, highlighting the need for alternative biomarkers. Disruptions in the circadian clock system may contribute to chronic insomnia, though the extent of these effects remains unclear. In this study, we investigate sleep and circadian rhythm-related alterations in chronic insomnia and its subtypes, ISSD and insomnia with normal sleep duration (INSD), by assessing plasma cortisol, wrist and axillary temperature, and clock gene expression in peripheral blood mononuclear cells (PBMCs). Additionally, we use machine learning to identify the most relevant clock genes for detecting insomnia and classifying its subtypes. Chronic insomnia patients exhibited reduced body temperature rhythms, elevated cortisol levels during wake before sleep, and significant alterations in clock gene expression, including in <i>BMAL1</i>, <i>PER1-2</i>, <i>REV-ERBα</i>, and <i>REV-ERBβ</i>, compared to controls. Most alterations were more significant in the ISSD group. Moreover, associations between clock gene expression, sleep-related parameters and Insomnia Severity Index (ISI) scores were identified. Using machine learning, we identified three genes as sensitive biomarkers distinguishing chronic insomnia from controls and differentiating between ISSD and INSD subtypes. Our findings suggest that circadian markers and machine learning could improve understanding of chronic insomnia and aid biomarker discovery for diagnosis.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Clock gene signature predicts insomnia and links to sleep/circadian parameters

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月1日

查看全文
  • Catarina Carvalhas-Almeida,
  • João Alves,
  • Tiago Davi,
  • Barbara Santos,
  • Laetitia Gaspar,
  • Rodrigo F. N. Ribeiro,
  • Joana Serra,
  • Mafalda Ferreira,
  • Joaquim Moita,
  • Amita Sehgal,
  • Cláudia Cavadas,
  • Ana Rita Álvaro

摘要

Chronic Insomnia is a prevalent sleep disorder that remains difficult to diagnose due to subjective symptoms and heterogeneous presentations. The most severe form, insomnia with short sleep duration (ISSD), is defined by a total sleep time of less than six hours on polysomnography. However, objective assessments are rarely recommended in diagnostic guidelines, highlighting the need for alternative biomarkers. Disruptions in the circadian clock system may contribute to chronic insomnia, though the extent of these effects remains unclear. In this study, we investigate sleep and circadian rhythm-related alterations in chronic insomnia and its subtypes, ISSD and insomnia with normal sleep duration (INSD), by assessing plasma cortisol, wrist and axillary temperature, and clock gene expression in peripheral blood mononuclear cells (PBMCs). Additionally, we use machine learning to identify the most relevant clock genes for detecting insomnia and classifying its subtypes. Chronic insomnia patients exhibited reduced body temperature rhythms, elevated cortisol levels during wake before sleep, and significant alterations in clock gene expression, including in BMAL1, PER1-2, REV-ERBα, and REV-ERBβ, compared to controls. Most alterations were more significant in the ISSD group. Moreover, associations between clock gene expression, sleep-related parameters and Insomnia Severity Index (ISI) scores were identified. Using machine learning, we identified three genes as sensitive biomarkers distinguishing chronic insomnia from controls and differentiating between ISSD and INSD subtypes. Our findings suggest that circadian markers and machine learning could improve understanding of chronic insomnia and aid biomarker discovery for diagnosis.